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Class-incremental learning is a challenging problem, where the goal is to train a model that can classify data from an increasing number of classes over time.
Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Handbook for Automatic Computation: Volume II: Linear Algebra, pp. 134–151. Springer (1971)
1971
Earlier work this paper cites.
Van Ness, J.: On the dominance of non-parametric bayes rule discriminant algorithms in high dimensions. Pattern Recognition 12
1980
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
2011
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114
2017
Earlier work this paper cites.
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40
2017
Earlier work this paper cites.
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40
2017
Earlier work this paper cites.
Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. Adv. Neural Inform. Process. Syst. 30
2017
Earlier work this paper cites.
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2001–2010 (2017)
2017
Earlier work this paper cites.
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: Eur. Conf. Comput. Vis. pp. 139–154 (2018)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: Podnet: Pooled outputs distillation for small-tasks incremental learning. In: Eur. Conf. Comput. Vis. pp. 86–102. Springer (2020)
2020
Earlier work this paper cites.
Geng, C., Huang, S.j., Chen, S.: Recent advances in open set recognition: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 43
2020
Earlier work this paper cites.
Hayes, T.L., Kanan, C.: Lifelong machine learning with deep streaming linear discriminant analysis. In: IEEE Conf. Comput. Vis. Pattern Recog. Worksh. pp. 220–221 (2020)
2020
Earlier work this paper cites.
Liu, Y., Su, Y., Liu, A.A., Schiele, B., Sun, Q.: Mnemonics training: Multi-class incremental learning without forgetting. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 12245–12254 (2020)
2020
Earlier work this paper cites.
Zhang, J., Zhang, J., Ghosh, S., Li, D., Tasci, S., Heck, L., Zhang, H., Kuo, C.C.J.: Class-incremental learning via deep model consolidation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1131–1140 (2020)
2020
Earlier work this paper cites.
Choi, Y., El-Khamy, M., Lee, J.: Dual-teacher class-incremental learning with data-free generative replay. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 3543–3552 (2021)
2021
Earlier work this paper cites.
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell. 44
2021
Earlier work this paper cites.
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., Gilmer, J.: The many faces of robustness: A critical analysis of out-of-distribution generalization. Int. Conf. Comput. Vis. (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: Int. Conf. Mach. Learn. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 3014–3023 (2021)
2021
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., Qiao, Y.: Clip-adapter: Better vision-language models with feature adapters. Int. J. Comput. Vis. pp. 1–15 (2023)
2023
Later among the works it cites.
Khan, M.G.Z.A., Naeem, M.F., Van Gool, L., Stricker, D., Tombari, F., Afzal, M.Z.: Introducing language guidance in prompt-based continual learning. In: Int. Conf. Comput. Vis. pp. 11463–11473 (2023)
2023
Later among the works it cites.
Lee, K.Y., Zhong, Y., Wang, Y.X.: Do pre-trained models benefit equally in continual learning? In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 6485–6493 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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Cited alongside, same era.
Zhou, D.W., Ye, H.J., Zhan, D.C.: Co-transport for class-incremental learning. In: ACM Int. Conf. Multimedia. pp. 1645–1654 (2021)
2021
Cited alongside, same era.
Zhu, F., Zhang, X.Y., Wang, C., Yin, F., Liu, C.L.: Prototype augmentation and self-supervision for incremental learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5871–5880 (2021)
2021
Cited alongside, same era.
Douillard, A., Ramé, A., Couairon, G., Cord, M.: Dytox: Transformers for continual learning with dynamic token expansion. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 9285–9295 (2022)
2022
Cited alongside, same era.
Gao, Q., Zhao, C., Ghanem, B., Zhang, J.: R-dfcil: Relation-guided representation learning for data-free class incremental learning. In: Eur. Conf. Comput. Vis. pp. 423–439. Springer (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., Van De Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. IEEE Trans. Pattern Anal. Mach. Intell. 45
2022
Cited alongside, same era.
Ostapenko, O., Lesort, T., Rodriguez, P., Arefin, M.R., Douillard, A., Rish, I., Charlin, L.: Continual learning with foundation models: An empirical study of latent replay. In: Conference on Lifelong Learning Agents. pp. 60–91. PMLR (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Luo, Z., Liu, Y., Schiele, B., Sun, Q.: Class-incremental exemplar compression for class-incremental learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 11371–11380 (2023)
2023
Later among the works it cites.
Malepathirana, T., Senanayake, D., Halgamuge, S.: Napa-vq: Neighborhood-aware prototype augmentation with vector quantization for continual learning. In: Int. Conf. Comput. Vis. pp. 11674–11684 (2023)
2023
Later among the works it cites.
Sarfraz, F., Arani, E., Zonooz, B.: Error sensitivity modulation based experience replay: Mitigating abrupt representation drift in continual learning. In: Int. Conf. Learn. Represent. (2023), https://openreview.net/forum?id=zlbci7019Z3
2023
Later among the works it cites.
Smith, J.S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., Kira, Z.: Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 11909–11919 (2023)
2023
Later among the works it cites.
Smith, J.S., Tian, J., Halbe, S., Hsu, Y.C., Kira, Z.: A closer look at rehearsal-free continual learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2409–2419 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Sun, Z., Mu, Y., Hua, G.: Regularizing second-order influences for continual learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 20166–20175 (2023)
2023
Later among the works it cites.
Tang, Y.M., Peng, Y.X., Zheng, W.S.: When prompt-based incremental learning does not meet strong pretraining. In: Int. Conf. Comput. Vis. pp. 1706–1716 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zheng, Z., Ma, M., Wang, K., Qin, Z., Yue, X., You, Y.: Preventing zero-shot transfer degradation in continual learning of vision-language models. In: Int. Conf. Comput. Vis. pp. 19125–19136 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
McDonnell, M.D., Gong, D., Parvaneh, A., Abbasnejad, E., van den Hengel, A.: Ranpac: Random projections and pre-trained models for continual learning. Adv. Neural Inform. Process. Syst. 36
2024
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